Jupyter Notebook for Data Science Teams Training Video

Informazione importanti

Corso

Online

Quando:Da definire

Descrizione

A Practical Training Course That Teaches Real World Skills In this project-based Jupyter Notebook for Data Science Teams video tutorial series, you'll quickly have relevant skills for real-world applications. Follow along with our expert instructor in this training course to get: Concise, informative and broadcast-quality Jupyter Notebook for Data Science Teams training videos delivered to your desktop The ability to learn at your own pace with our intuitive, easy-to-use interface A quick grasp of even the most complex Jupyter Notebook for Data Science Teams subjects because they're broken into simple, easy to follow tutorial videos Practical working files further enhance the learning process and provide a degree of retention that is unmatched by any other form of Jupyter Notebook for Data Science Teams tutorial, online or offline...so you'll know the exact steps for your own projects. Course Fast Facts: Only 3 hours to complete this course 36 tutorial videos Expert instructors lead each course Download to any Windows PC or Mac and save for viewing off line Course is accessible 24/7 from any computer once downloaded You can study from home or at work at your own pace in your own time Course Description In this Jupyter Notebook for Data Science Teams training course, expert author Jonathan Whitmore will teach you about Jupyter Notebook extensions, widgets, and team sharing. This course is designed for data scientists who need to collaborate on projects.

You will start by learning how to install and set up the Jupyter Notebook, as well as how to set up Git and GitHub accounts. From there, Jonathan will teach you about Jupyter Notebook features, including extensions, SQL Magic and Pandas, and interactive widgets. This video tutorial also covers how to share notebooks with a team. Finally, you will run through an example of using a single Git repository for a team data science project from start to...

Informazione importanti

Requisiti: System Requirements - Digital Download Digital Download: Microsoft Windows XP or higher, Mac OS X 10.4 or higher. Minimum screen resolution of 1024x768 Digital Download specific requirements: Between 1GB and 6GB of available hard drive space (depending on the training course) An Internet connection with sufficient bandwidth. You must have at least a 56K modem connection (Broadband recommended). Most modern ADSL and Cable internet solutions will be sufficient. Do I need...

Sedi

Dove e quando

Inizio

Luogo

Da definire

Online

Cosa impari in questo corso?

SQL

Magic

Project

Team Training

Skills and Training

Programma

01. Introduction

Introduction And Course Overview

About The Author

0103 How To Access Your Working Files

02. Setting Up Environment

Installing The Jupyter Notebook And Setup

Setting Up Git And GitHub Account

03. Jupyter Notebook Features

0301 Standard Browser Use

0302 Installing Notebook Extensions

0303 More On Notebook Extensions

0304 SQL Magic And Pandas

0305 Conda Environments

0306 R In Jupyter Notebook

0307 Autocreate Documents In HTML Or PDF

0308 Interactive Widgets

0309 Bleeding Edge - JupyterHub

04. Sharing Notebooks With A Team

0401 Organizing A Workflow

0402 Lab Vs. Deliverable Notebook

0403 Directory Structure And Naming Conventions

0404 Version Control

05. Project - Data Science With The Notebook End-To-End Example

0501 Get Data

0502 Load The Data

0503 Initial Data Cleaning

0504 Creating A New Github Repository

0505 Version Control

0506 Exploratory Data Analysis - Regression Plotting

0507 Exploratory Data Analysis - Variable Transformations

0508 Git Branch Store Data Cleaned Pipeline

0509 Feature Engineering

0510 Random Forest Prediction And Evaluation

0511 Final Analysis Cleanup

0512 Pull Request, Peer Review, And Merge With Master

06. Project - Data Science: Statistics And Data Visualizations

0601 Initial Data Visualization

0602 Advanced Pandas Plotting

0603 Advanced Seaborn Plotting

0604 Statsmodels Analysis - Part 1

0605 Statsmodels Analysis - Part 2

07. Conclusion

0701 Resources And Where To Go From Here

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